# ## 原创，转载，广告文章数的占比
# In[88]:
# 三种类型的文章数
original_num = article[article['是否原创'] == '是'].shape[0]
reprint_num = article[article['是否广告'] == '是'].shape[0]
advertising_num = article[article['是否转载'] == '是'].shape[0]
# 占比圆环图
from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.globals import ThemeType
v = ['原创', '广告', '转载']
c = (Pie(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE, chart_id=1)).add(
"",
[list(z) for z in zip(v, [original_num, reprint_num, advertising_num])],
radius=["40%", "75%"],
).set_global_opts(
title_opts=opts.TitleOpts(title="转载，广告，原创占比"),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))
c.render('../output/转载，广告，原创占比.html')
c.render_notebook()
# ## 文章标题用词状况
# In[89]:
offset=80,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(
color="#5793f3")),
axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
legend_opts=opts.LegendOpts(pos_left="65%"),
))
line1 = (Line().add_xaxis(["{}月".format(i) for i in range(1, 13)]).add_yaxis(
"平均温度 1",
[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],
color="#675bba",
label_opts=opts.LabelOpts(is_show=False),
xaxis_index=1,
yaxis_index=5,
))
overlap_1 = bar.overlap(line)
overlap_2 = bar1.overlap(line1)
c = (Grid(init_opts=opts.InitOpts(width="1200px", height="800px")).add(
overlap_1,
grid_opts=opts.GridOpts(pos_right="58%"),
is_control_axis_index=True).add(overlap_2,
grid_opts=opts.GridOpts(pos_left="58%"),
is_control_axis_index=True))
c.width = "100%"
put_html(c.render_notebook())
import pyecharts.options as opts # 导入pyecharts库
from pyecharts.charts import Line
num = [0.7073, 0.6472, 0.8294, 0.8315] # 导入数据
num2 = [0.6131, 0.7733, 0.8283, 0.8237]
num3 = [0.6256, 0.7551, 0.8286, 0.8269]
lab = ["第一阶段", "第二阶段", "第三阶段", "第四阶段"]
(Line(init_opts=opts.InitOpts(width='720px',
height='320px')) # 指定画布大小,括号内为空则默认大小
.add_xaxis(xaxis_data=lab).add_yaxis("湖北", num).add_yaxis(
"其他省份", num2,
symbol="triangle").add_yaxis("总体心态", num3, symbol="").set_global_opts(
title_opts=opts.TitleOpts(title="积极心态变化图")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False))).render('积极心态折线图.html')
}]),
xaxis_opts=opts.AxisOpts(
grid_index=1,
type_="category",
boundary_gap=True,
position="top",
axisline_opts=opts.AxisLineOpts(is_on_zero=True)),
yaxis_opts=opts.AxisOpts(is_inverse=False,
name="Value",
name_gap=25),
legend_opts=opts.LegendOpts(pos_bottom='10%',
pos_right='50'))
# In[11]:
grid = Grid(init_opts=opts.InitOpts(width="1024px", height="768px"))
grid.add(chart=l1,
grid_opts=opts.GridOpts(pos_left=50, pos_right=50, height="35%"))
grid.add(chart=l2,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top="55%",
height="35%"))
grid.render_notebook()
# In[12]:
# get_data_yahoo(inst_ticker, start_date, end_date)
sp = pdr.get_data_yahoo('^GSPC', '17-Nov-19')
ns = pdr.get_data_yahoo('^IXIC', '17-Nov-19')
def day_sxdl(self, x, mc, a, b, c, k):
c = (Line(
init_opts=opts.InitOpts(width="1000px", height="500px")
).add_xaxis(x).add_yaxis("电压A", a, is_smooth=True).add_yaxis(
"电压B", b, is_smooth=True).add_yaxis(
"电压C", c, is_smooth=True).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
title_opts={
'text': mc,
'subtext': None
},
xaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(rotate=-90),
max_interval=366,
type_="category",
axispointer_opts=opts.AxisPointerOpts(is_show=True,
type_="shadow"),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} v"),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
toolbox_opts=opts.
ToolboxOpts(feature=opts.ToolBoxFeatureOpts(
save_as_image=opts.ToolBoxFeatureSaveAsImageOpts(
pixel_ratio=2, background_color='white'),
restore=opts.ToolBoxFeatureRestoreOpts(is_show=False),
data_view=opts.ToolBoxFeatureDataViewOpts(
is_show=False),
data_zoom=opts.ToolBoxFeatureDataZoomOpts(
is_show=False),
magic_type=opts.ToolBoxFeatureMagicTypeOpts(
is_show=False),
brush=opts.ToolBoxFeatureBrushOpts(type_='clear'),
))).set_series_opts(label_opts=opts.LabelOpts(
is_show=False), ).extend_axis(
yaxis=opts.AxisOpts(name='方差',
axislabel_opts=opts.LabelOpts(
formatter="{value} "))))
line1 = Line().add_xaxis(x).add_yaxis(
"方差",
k,
yaxis_index=1,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(
color="black", width=2, type_="solid")).set_series_opts(
label_opts=opts.LabelOpts(is_show=False), )
c.overlap(line1)
return c
# def day_loss_1(self,x,y,t,st,z,m):
# c = self.dayloss_line_base(x,y,t,st)
# c = c.extend_axis(
# yaxis=opts.AxisOpts(
# axislabel_opts=opts.LabelOpts(formatter="{value} kWh")
# )
# ).extend_axis(
# yaxis=opts.AxisOpts(
# name = '数据完整率',
# position = 'right',
# offset = 62,
# axislabel_opts=opts.LabelOpts(formatter="{value} "),
# axisline_opts=opts.AxisLineOpts(
# linestyle_opts=opts.LineStyleOpts(color="#675bba")
# ),
# )
# )
# line = Line().add_xaxis(x).add_yaxis("供入电量", z,yaxis_index=1,is_smooth=True).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# line_1 = Line().add_xaxis(x).add_yaxis("数据完整率", m,yaxis_index=2).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# c.overlap(line)
# c.overlap(line_1)
# return c
# def day_loss_2(self,x,y,t,st,a,b):
# c = self.dayloss_line_base(x,y,t,st)
# c = c.extend_axis(
# yaxis=opts.AxisOpts(
# axislabel_opts=opts.LabelOpts(formatter="{value} kWh")
# )
# )
# line = Line().add_xaxis(a).add_yaxis("用电量", b,yaxis_index=1,is_smooth=True).set_series_opts(
# label_opts=opts.LabelOpts(is_show=False), )
# c.overlap(line)
# return c
# class electricity():
# """docstring for electricity"""
# def __init__(self,df):
# self.df = df
# def day_data(self,a):
# tq = self.df[self.df.测量点号==a][['数据时间','正向']].set_index('数据时间').sort_values(by='数据时间')
# x = [i[-5:] for i in tq.index]
# y = [float(i[0]) for i in tq.values]
# t = a
# return x,y,t
# def measure_num(self):
# num = set(i for i in self.df.测量点号)
# num = list(num)[1:]
# return num
# class relation(object):
# """docstring for relation"""
# def curr_relation(self,x,y,a,b):
# lineloss = pd.Series(y,index=x)
# day_electricity = pd.Series(b,index= a)
# c = lineloss.corr(day_electricity)
# # c1 = lineloss.cov(day_electricity)
# return c
# IO = '石鼓12月.xls'
# k = []
# day_loss = data(IO)
# tq_index = day_loss.day_lineloss_index()
# for i in tq_index:
# x,y,a,z,m = data(IO).day_lineloss(i)
# nc = relation().curr_relation(x,y,x,z)
# c = drawing().day_loss_1(x,y, a+ str(nc),z,m)
# k.append(c)
# drawing().all_day_lineloss(k,'石鼓12')
# IO = '石鼓12月.xls'
# name = '宝丰12.xlsx'
# num = electricity(name).measure_num()
# day_loss = data(IO)
# a,b,j,z,m = data(IO).day_lineloss('宝丰公用台变')
# k1 = []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# c1 = drawing().day_loss_2(a,b,str(t)+' & '+str(c),x,y)
# k1.append(c1)
# ny = np.array(electricity(name).day_data(92)[1]) + np.array(electricity(name).day_data(37)[1])
# c = relation().curr_relation(a,b,x,ny)
# c1 = drawing().day_loss_2(a,b,'9237'+str(c),x,ny)
# k1.append(c1)
# drawing().all_day_lineloss(k1,'宝丰12')
# k2 = []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# k2.append(c)
# print(str(c) + 10*'-'+str(i))
# print(10*'--')
# k3 = []
# nyy= []
# for i in num:
# x,y,t = electricity(name).day_data(i)
# c = relation().curr_relation(a,b,x,y)
# print(c[0])
# if c[0] > 0.5:
# nyy.append(np.array(y))
# c1 = drawing().day_loss_2(a,b,t,x,y)
# k3.append(c1)
# c2 = drawing().day_loss_2(a,b,'all',x,sum(nyy))
# k3.append(c2)
# drawing().all_day_lineloss(k3,'test_class_5')
# c = relation().curr_relation(a,b,x,sum(nyy))
# print(c)
def show_pyecharts():
x_data = [
'0.0', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9',
'1.0'
]
y_data = [
'1533', '573', '502', '491', '520', '556', '577', '788', '1252',
'20564', '6393'
]
#情感分析柱状图
bar = (Bar().add_xaxis(x_data).add_yaxis(
"情感分析",
y_data,
label_opts=opts.LabelOpts(is_show=False),
color='#3498DB').set_global_opts(title_opts={
"text": "台海舆论情感分析",
"subtext": "大于0.5为正面评价，小于0.5为负面评价"
}))
#台湾网饼图
x_data2 = [
"文化", "经贸", "媒体专栏", "网友专栏", "两岸专家", "两岸", "台商", "部委", "台海时事", "网友快言",
"海峡时评", "两岸快评"
]
y_data2 = [553, 553, 35, 39, 448, 465, 553, 321, 553, 406, 553, 556]
pie = (Pie(init_opts=opts.InitOpts(theme=ThemeType.CHALK)).add(
series_name="中国台湾网",
data_pair=[list(z) for z in zip(x_data2, y_data2)],
radius=["50%", "70%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_colors([
"#E8F8F5", "#D1F2EB", "#A3E4D7", "#76D7C4", "#48C9B0", "#1ABC9C",
"#17A589", "#148F77", "#117864", "#0E6251F", "#73C6B6", "#45B39D"
]).set_global_opts(legend_opts=opts.LegendOpts(
pos_left="legft", orient="vertical")).set_series_opts(
tooltip_opts=opts.TooltipOpts(
trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"), ))
#词云图
word = (WordCloud().add(
"",
list1,
word_size_range=[20, 100],
shape=SymbolType.DIAMOND,
textstyle_opts=opts.TextStyleOpts(font_family="cursive"),
))
#折线图1
y2[0], y2[1], y2[2] = None, None, None
y4[1] = None
line = (Line().add_xaxis(xaxis_data=x1).add_yaxis(
series_name="微博",
symbol="emptyCircle",
is_symbol_show=True,
color="#F2D7D5",
y_axis=y1,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="环球网",
symbol="emptyCircle",
is_symbol_show=True,
color="#C0392B",
y_axis=y2,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中国台湾网",
symbol="emptyCircle",
is_symbol_show=True,
color="#641E16",
y_axis=y3,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="知乎",
symbol="emptyCircle",
is_symbol_show=True,
color="#9B59B6",
y_axis=y4,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中国日报网",
symbol="emptyCircle",
is_symbol_show=True,
color="#512E5F",
y_axis=y5,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="中新网",
symbol="emptyCircle",
is_symbol_show=True,
color="#2980B9",
y_axis=y6,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).
add_yaxis(
series_name="今日头条",
symbol="emptyCircle",
is_symbol_show=True,
color="#154360",
y_axis=y7,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="光明网",
symbol="emptyCircle",
is_symbol_show=True,
color="#3498DB",
y_axis=y8,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="凤凰网",
symbol="emptyCircle",
is_symbol_show=True,
color="#1ABC9C",
y_axis=y9,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=3)).add_yaxis(
series_name="新华网",
symbol="emptyCircle",
is_symbol_show=True,
color="#0E6251",
y_axis=y10,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(
width=3)).set_global_opts(
tooltip_opts=opts.TooltipOpts(
trigger="axis"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(
is_show=True),
splitline_opts=opts.SplitLineOpts(
is_show=True),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(
is_on_zero=False,
linestyle_opts=opts.LineStyleOpts(
color="#d14a61"))),
))
#平台占比饼图
x_data3 = [
"微博", "知乎", "中国台湾网", "环球网", "日报网", "中新网", "今日头条", "光明网", "凤凰网", "新华网"
]
y_data3 = [5401, 157, 5035, 3245, 4296, 5574, 1891, 3131, 1052, 1997]
data_pair = [list(z) for z in zip(x_data3, y_data3)]
data_pair.sort(key=lambda x: x[1])
pie2 = (Pie(init_opts=opts.InitOpts(theme=ThemeType.ROMA)).add(
series_name="访问来源",
data_pair=data_pair,
rosetype="radius",
radius="55%",
center=["50%", "50%"],
label_opts=opts.LabelOpts(is_show=False, position="center"),
).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{d}%")))
#折线图2
line2 = (Line().add_xaxis(xaxis_data=x_data4).add_yaxis(
series_name="热度走势",
y_axis=y_data4,
color="#FF69B4",
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="max")]),
symbol="emptyCircle",
is_symbol_show=True,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=True),
).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
xaxis_opts=opts.AxisOpts(type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(rotate=45)),
))
#条形图
line3 = (Bar().add_xaxis(x_data5).add_yaxis(
"平均值",
y_data5,
label_opts=opts.LabelOpts(is_show=False),
color='#6A5ACD').reversal_axis().extend_axis(yaxis=opts.AxisOpts(
type_='value',
name='转发次数',
position='left',
)).set_global_opts(
title_opts={"text": "评论与转发关系图"},
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
formatter='{value}(评论数量)')),
))
#圆
radius = (Polar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_schema(
radiusaxis_opts=opts.RadiusAxisOpts(data=x_data6, type_="category"),
angleaxis_opts=opts.AngleAxisOpts(is_clockwise=True, max_=100000),
).add("点赞数量", y_data6, type_="bar").set_global_opts(
title_opts=opts.TitleOpts(title="")).set_series_opts(
label_opts=opts.LabelOpts(is_show=True)))
return render_template("index2.html",
bar_data=bar.dump_options(),
word_data=word.dump_options(),
pie_data=pie.dump_options(),
pie2_data=pie2.dump_options(),
line_data=line.dump_options(),
line2_data=line2.dump_options(),
line3_data=line3.dump_options(),
radius_data=radius.dump_options())
137.7008290515613,
199.78999859299336,
],
[
0.5370189113081292,
50.44519588101707,
98.08928065026996,
139.8482090057953,
197.20820681141507,
],
],
"outliers": [],
},
]
(Boxplot(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=axis_data).add_yaxis(
series_name="category0",
y_axis=data[0]["boxData"],
tooltip_opts=opts.TooltipOpts(
formatter=JsCode("""function(param) { return [
'Experiment ' + param.name + ': ',
'upper: ' + param.data[0],
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
].join('<br/>') }""")),
).add_yaxis(
series_name="category1",
y_axis=data[1]["boxData"],
import pyecharts.options as opts
from pyecharts.charts import MapGlobe
from pyecharts.faker import POPULATION
data = [x for _, x in POPULATION[1:]]
low, high = min(data), max(data)
c = (
MapGlobe(init_opts=opts.InitOpts(width='1800px',
height='960px',
bg_color='#00BBFF')) #
.add_schema().add(
maptype="world",
series_name="World Population",
data_pair=POPULATION[1:],
is_map_symbol_show=True,
label_opts=opts.LabelOpts(is_show=True),
).set_global_opts(
title_opts=opts.TitleOpts(title="3D 地球示例"),
# 设置地球属性
visualmap_opts=opts.VisualMapOpts(
min_=low,
max_=high,
range_text=["max", "min"],
is_calculable=True,
range_color=["lightskyblue", "yellow", "orangered"],
)).render("world_map_3d.html"))
)
)
pie = (
Pie()
.add(
"",
[list(z) for z in zip(x, ys[i])],
radius=[60, 0],
center=["70%", "20%"],
label_opts=opts.LabelOpts(is_show=False)
)
.set_global_opts(
legend_opts=opts.LegendOpts(
type_="scroll", pos_top="10%", pos_left="80%", orient="vertical"
),
)
)
grid = (
Grid(init_opts=opts.InitOpts(width="1800px", height="800px"))
.add(bar, grid_opts=opts.GridOpts(pos_left="5%", width=500, height=200))
.add(pie, grid_opts=opts.GridOpts(pos_right="0%", pos_top='80%', width=200, height=250))
)
tl.add(grid, time[i])
tl.add_schema(
pos_top="55%",
pos_left='0%',
label_opts=opts.LabelOpts(rotate=45,position='Bottom',interval=None,horizontal_align='left',distance=0)
)
tl.render("tabRE.html")
import pyecharts.options as opts
from pyecharts.charts import Radar
"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.apache.org/examples/editor.html?c=radar
目前无法实现的功能:
1、雷达图周围的图例的 textStyle 暂时无法设置背景颜色
"""
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
(Radar(init_opts=opts.InitOpts(bg_color="#CCCCCC")).add_schema(
schema=[
opts.RadarIndicatorItem(name="销售（sales）", max_=6500),
opts.RadarIndicatorItem(name="管理（Administration）", max_=16000),
opts.RadarIndicatorItem(name="信息技术（Information Technology）",
max_=30000),
opts.RadarIndicatorItem(name="客服（Customer Support）", max_=38000),
opts.RadarIndicatorItem(name="研发（Development）", max_=52000),
opts.RadarIndicatorItem(name="市场（Marketing）", max_=25000),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
).add(
series_name="预算分配（Allocated Budget）",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
).add(
"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://www.echartsjs.com/examples/editor.html?c=line-marker
目前无法实现的功能:
1、最低气温的最高值暂时无法和 Echarts 的示例完全复刻
"""
week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
high_temperature = [11, 11, 15, 13, 12, 13, 10]
low_temperature = [1, -2, 2, 5, 3, 2, 0]
(
Line(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add_xaxis(xaxis_data=week_name_list)
.add_yaxis(
series_name="最高气温",
y_axis=high_temperature,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.add_yaxis(
ql.cursor = ql.conn.cursor(cursor=ql.pymysql.cursors.DictCursor)
sql = 'select date, start_price, max_price, min_price, stop_price from abc001_defect;'
ql.cursor.execute(sql)
ret = ql.cursor.fetchall()
print(ret)
x_data = []
y_data = []
for i in ret:
str_date = datetime.datetime.strftime(i['date'], '%Y-%m-%d')
x_data.append(str_date)
y_data.append(
[i['start_price'], i['max_price'], i['min_price'], i['stop_price']])
c = (Kline(init_opts=opts.InitOpts(width="1440px", height="800px")).add_xaxis(
xaxis_data=x_data).add_yaxis(
"kline",
y_data,
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#8A0000",
border_color0="#008F28",
),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
def barChart(df):
type_of_plot = st.selectbox("Select Type of Plot",
["BAR1", "BAR2", "BAR3", "BAR4", "BAR5"])
all_columns = df.columns
#st.success("Generating Customizable Plot of {} for {}".format(type_of_plot))
if type_of_plot == 'BAR1':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
columns_to_plot2 = st.selectbox("Select 3 column",
all_columns,
key='c')
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
other_var1 = df[columns_to_plot2].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1200px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var)
bar.add_yaxis(columns_to_plot2, other_var1)
bar.set_global_opts(toolbox_opts=opts.ToolboxOpts())
bar.reversal_axis()
bar.set_series_opts(
label_opts=opts.LabelOpts(position="right"),
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]),
)
#bar.set_global_opts(title_opts=opts.TitleOpts(title="Bar-Test Rendered Pictures"))
# st.button("Generate Plot")
return bar
if type_of_plot == 'BAR2':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
cje = df[columns_to_plot].tolist()
other_var = df[columns_to_plot1].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1800px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=65)),
brush_opts=opts.BrushOpts())
bar.set_series_opts(markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]), )
return bar
if type_of_plot == 'BAR3':
columns_to_plot = st.selectbox("Select 1 column", all_columns, key='a')
columns_to_plot1 = st.selectbox("Select 2 column",
all_columns,
key='b')
columns_to_plot2 = st.selectbox("Select 3 column",
all_columns,
key='c')
columns_to_plot3 = st.selectbox("Select 4 column",
all_columns,
key='d')
columns_to_plot4 = st.selectbox("Select 5 column",
all_columns,
key='e')
cje = df[columns_to_plot].tolist()
other_var1 = df[columns_to_plot1].tolist()
other_var2 = df[columns_to_plot2].tolist()
other_var3 = df[columns_to_plot3].tolist()
other_var4 = df[columns_to_plot4].tolist()
bar = Bar(init_opts=opts.InitOpts(width='900px', height='1200px'))
bar.add_xaxis(cje)
bar.add_yaxis(columns_to_plot1, other_var1)
bar.add_yaxis(columns_to_plot2, other_var2)
bar.add_yaxis(columns_to_plot3, other_var3)
bar.add_yaxis(columns_to_plot4, other_var4)
bar.reversal_axis()
#bar.add_yaxis(columns_to_plot1, other_var, gap="0%")
bar.set_global_opts(
toolbox_opts=opts.ToolboxOpts(),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=65)),
brush_opts=opts.BrushOpts())
grid = Grid()
grid.add(bar, grid_bottom="60%")
return bar
#%% md
### 商品销量排名top10 - 柱形图
#%%
# 导入包
from pyecharts.charts import Bar
from pyecharts import options as opts
# 计算top10店铺
shop_top10 = df.groupby('商品名')['销量'].sum().sort_values(
ascending=False).head(10)
# 绘制柱形图
bar0 = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
bar0.add_xaxis(shop_top10.index.tolist())
bar0.add_yaxis('sales_num', shop_top10.values.tolist())
bar0.set_global_opts(
title_opts=opts.TitleOpts(title='粽子商品销量Top10'),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
visualmap_opts=opts.VisualMapOpts(max_=shop_top10.values.max()))
bar0.render("粽子商品销量Top10.html")
#%% md
### 店铺销量排名top10 - 柱形图
#%%
# 导入包
def pyecharts(request):
"""dashboard view"""
# 工单数量统计
chart_dao = ChartDao()
data = chart_dao.workflow_by_date(30)
today = date.today()
one_month_before = today - relativedelta(days=+30)
attr = chart_dao.get_date_list(one_month_before, today)
_dict = {}
for row in data['rows']:
_dict[row[0]] = row[1]
value = [_dict.get(day) if _dict.get(day) else 0 for day in attr]
bar1 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar1.add_xaxis(attr)
bar1.add_yaxis("", value)
# 工单按组统计
data = chart_dao.workflow_by_group(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie1 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie1.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie1.add("", [list(z) for z in zip(attr, value)])
# 工单按人统计
data = chart_dao.workflow_by_user(30)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar2 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar2.add_xaxis(attr)
bar2.add_yaxis("", value)
# SQL语句类型统计
data = chart_dao.syntax_type()
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
pie2 = Pie()
pie2.set_global_opts(title_opts=opts.TitleOpts(title='SQL上线工单统计(类型)'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%"))
pie2.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie2.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(每日检索行数)
attr = chart_dao.get_date_list(one_month_before, today)
effect_data = chart_dao.querylog_effect_row_by_date(30)
effect_dict = {}
for row in effect_data['rows']:
effect_dict[row[0]] = int(row[1])
effect_value = [
effect_dict.get(day) if effect_dict.get(day) else 0 for day in attr
]
count_data = chart_dao.querylog_count_by_date(30)
count_dict = {}
for row in count_data['rows']:
count_dict[row[0]] = int(row[1])
count_value = [
count_dict.get(day) if count_dict.get(day) else 0 for day in attr
]
line1 = Line(init_opts=opts.InitOpts(width='600', height='380px'))
line1.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(selected_mode='single'))
line1.add_xaxis(attr)
line1.add_yaxis("检索行数",
effect_value,
is_smooth=True,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="average")]))
line1.add_yaxis("检索次数",
count_value,
is_smooth=True,
markline_opts=opts.MarkLineOpts(data=[
opts.MarkLineItem(type_="max"),
opts.MarkLineItem(type_="average")
]))
# SQL查询统计(用户检索行数)
data = chart_dao.querylog_effect_row_by_user(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie4 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie4.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie4.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
pie4.add("", [list(z) for z in zip(attr, value)])
# SQL查询统计(DB检索行数)
data = chart_dao.querylog_effect_row_by_db(30)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie5 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie5.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie5.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}", position="left"))
pie5.add("", [list(z) for z in zip(attr, value)])
# 慢查询db/user维度统计(最近1天)
data = chart_dao.slow_query_count_by_db_by_user(1)
attr = [row[0] for row in data['rows']]
value = [int(row[1]) for row in data['rows']]
pie3 = Pie(init_opts=opts.InitOpts(width='600', height='380px'))
pie3.set_global_opts(title_opts=opts.TitleOpts(title=''),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="15%",
pos_left="2%",
is_show=False))
pie3.set_series_opts(
label_opts=opts.LabelOpts(formatter="{b}: {c}", position="left"))
pie3.add("", [list(z) for z in zip(attr, value)])
# 慢查询db维度统计(最近1天)
data = chart_dao.slow_query_count_by_db(1)
attr = [row[0] for row in data['rows']]
value = [row[1] for row in data['rows']]
bar3 = Bar(init_opts=opts.InitOpts(width='600', height='380px'))
bar3.add_xaxis(attr)
bar3.add_yaxis("", value)
# 可视化展示页面
chart = {
"bar1": bar1.render_embed(),
"pie1": pie1.render_embed(),
"bar2": bar2.render_embed(),
"bar3": bar3.render_embed(),
"pie2": pie2.render_embed(),
"line1": line1.render_embed(),
"pie3": pie3.render_embed(),
"pie4": pie4.render_embed(),
"pie5": pie5.render_embed(),
}
# 获取统计数据
dashboard_count_stats = {
"sql_wf_cnt": SqlWorkflow.objects.count(),
"query_wf_cnt": QueryPrivilegesApply.objects.count(),
"user_cnt": Users.objects.count(),
"ins_cnt": Instance.objects.count()
}
return render(request, "dashboard.html", {
"chart": chart,
"count_stats": dashboard_count_stats
})
# 使用 defaultdict 统计出电影种类和对应电影数
counter = defaultdict(int)
for genre in genres:
for e in genre:
counter[e] += 1
print(counter)
# 一共有 29 种电影,对上面字典按种类数排序
counter_sorted = sorted(counter.items(), key=lambda x: x[1])
print(counter_sorted)
# 取电影种类数最多的前 10 进行分析
top10 = counter_sorted[-10:]
# 绘制前 10 最多种类数的柱状图，使用 pyecharts 绘制
x = [x for x, y in top10]
y = [y for x, y in top10]
bar = (
Bar(init_opts=opts.InitOpts(height='1200px'))
.add_xaxis(x)
.add_yaxis('电影种类名', y, category_gap='50%')
.reversal_axis()
.set_global_opts(title_opts=opts.TitleOpts(title="电影种类及影片数"),
toolbox_opts=opts.ToolboxOpts())
)
grid = (
Grid(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
.add(bar, grid_opts=opts.GridOpts(pos_left="30%"))
)
grid.render(path='./语法基础/res/html/movie1.html')
c = (
Pie()
.add(
line2.add_yaxis(
series_name='疑似数',
y_axis=suspect_num,
color='#fed0fc',
# 标签配置
label_opts=opts.LabelOpts(is_show=False),
# 线
linestyle_opts=opts.LineStyleOpts(width=1.2, color='#fed0fc'))
# 合并两个图
sumx = bar.overlap(line1).overlap(line2)
# 组合
all_ = Grid(init_opts=opts.InitOpts(
width='1750px', # 宽度
height='654px', # 高度
theme=ThemeType.PURPLE_PASSION,
))
all_.add(
sumx,
grid_opts=opts.GridOpts(pos_left='5%', pos_top='20%', pos_right='40%'),
is_control_axis_index=True,
)
# 生成
all_.render('./conv-19.html')
# 1、绘制2个柱状图 ---应该添加 3个新纵轴、 修改1个原来的纵轴(全局配置项)
# 2、绘制1个折线图
# 3、再绘制1个折线图
'M' k线+MACD
FieldName: string Dataframe中的字段名
[Field1,Field2,...] Dataframe中的字段名列表，将显示在一个区域
width: int #图表宽度 px
height:int #图表高度 px
klines:list #K线区域显示的数据，Dataframe中的字段名，如MA...
vline: list #Volume区域显示的数据，Dataframe中的字段名，如MA...
- sample:
chart=data.plot(area=[['small_pct','medium_pct','big_pct','super_pct'],'V','cci'],vlines=['vMA5','vMA10'],klines=['MA5','MA10'])
'''
self.klines=klines
self.vlines=vlines
grid = (
Grid(init_opts=opts.InitOpts(
width=str(width)+"px",
height=str(height)+"px",
animation_opts=opts.AnimationOpts(animation=False),
)
)
)
c=self.K()
iTop=10
iButton=10
iWindows=len(area)
iStep=0
if iWindows==0:
grid.add(c, grid_opts=opts.GridOpts(pos_top="2%",pos_bottom="10%"))
elif iWindows>1:
grid.add(c, grid_opts=opts.GridOpts(pos_top="2%",pos_bottom="50%"))
iStep=int(30/iWindows)
iButton=50
import pyecharts
from pyecharts.charts import Bar # 柱状图
from pyecharts import options as ops
from pyecharts.globals import ThemeType # 内置主题类型
bar = (
Bar(init_opts=ops.InitOpts(theme="")).add_xaxis(
["xxx", "juice", "14", "ts", "drake",
"young thug"]).add_yaxis("商家A", [100, 20, 36, 10, 75, 90])
# 全局配置项
.set_global_opts(
# 标题配置项
title_opts=ops.TitleOpts(
# 主标题
title="hello",
subtitle="hhh"),
# 图例配置项
legend_opts=ops.LegendOpts(is_show=True),
# 工具配置项
toolbox_opts=ops.ToolboxOpts(is_show=False),
# 视觉映射配置项
visualmap_opts=ops.VisualMapOpts(is_show=True,
# type_="color"
)))
bar.render("柱状图.html")
# 使用 options 配置项，在 pyecharts 中，一切皆 Options。
# 全局配置项可通过 set_global_opts 方法设置
"合肥": [117.27, 31.86],
"武汉": [114.31, 30.52],
"大庆": [125.03, 46.58],
}
def convert_data():
res = []
for i in range(len(data)):
geo_coord = geoCoordMap[data[i][0]]
geo_coord.append(data[i][1])
res.append([data[i][0], geo_coord])
return res
(BMap(init_opts=opts.InitOpts(width="1400px", height="800px")).add(
type_="effectScatter",
series_name="已点亮人数",
data_pair=convert_data(),
symbol_size=10,
effect_opts=opts.EffectOpts(),
label_opts=opts.LabelOpts(formatter="{b}", position="right",
is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color="white"),
).add_schema(
baidu_ak="FAKE_AK",
center=[104.114129, 37.550339],
zoom=5,
is_roam=True,
map_style={
"styleJson": [
else:
break
return temp
def surface3d_data():
for t0 in float_range(-3, 3, 0.05):
y = t0
for t1 in float_range(-3, 3, 0.05):
x = t1
z = math.sin(x ** 2 + y ** 2) * x / 3.14
yield [x, y, z]
(
Surface3D(init_opts=opts.InitOpts(width="1600px", height="800px"))
.add(
series_name="",
shading="color",
data=list(surface3d_data()),
xaxis3d_opts=opts.Axis3DOpts(type_="value"),
yaxis3d_opts=opts.Axis3DOpts(type_="value"),
grid3d_opts=opts.Grid3DOpts(width=100, height=40, depth=100),
)
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(
dimension=2,
max_=1,
min_=-1,
range_color=[
"#313695",
def plot_lines(self, x_data, y_data, filename_, series_name='x1_dist'):
filename = self.plot_line_path + filename_
background_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
)
area_color_js = (
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
)
c = (Line(
init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))
).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name=series_name,
y_axis=y_data,
is_smooth=True,
is_symbol_show=True,
symbol="circle",
symbol_size=6,
linestyle_opts=opts.LineStyleOpts(color="#fff"),
label_opts=opts.LabelOpts(is_show=True,
position="top",
color="white"),
itemstyle_opts=opts.ItemStyleOpts(color="red",
border_color="#fff",
border_width=3),
tooltip_opts=opts.TooltipOpts(is_show=False),
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js),
opacity=1),
).set_global_opts(
title_opts=opts.TitleOpts(
title="show %s in one image" % series_name,
pos_top="5%",
pos_left="center",
title_textstyle_opts=opts.TextStyleOpts(color="#fff",
font_size=16),
),
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=25,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
yaxis_opts=opts.AxisOpts(
type_="value",
position="right",
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")),
axistick_opts=opts.AxisTickOpts(
is_show=True,
length=15,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
),
splitline_opts=opts.SplitLineOpts(
is_show=True,
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")),
),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100),
opts.DataZoomOpts(type_="inside", range_start=0,
range_end=100),
],
legend_opts=opts.LegendOpts(is_show=False),
))
c.render(filename)
"""
* @File: 05-使用主题.py
* @Author: CSY - 25809
* @Date: 2020/5/27 - 16:54
* @Project: Python
"""
from pyecharts.charts import Bar
from pyecharts import options as opts
# 内置主题类型可查看 pyecharts.globals.ThemeType
from pyecharts.globals import ThemeType
bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis(
["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋",
"袜子"]).add_yaxis("商家A", [5, 20, 36, 10, 75, 90]).add_yaxis(
"商家B", [15, 6, 45, 20, 35, 66]).set_global_opts(
title_opts=opts.TitleOpts(title="主标题", subtitle="副标题")))
bar.render()
map_chart = get_Map(time, data)
bar_chart = get_Bar(time, data)
g = (Grid(init_opts=opts.InitOpts(width='1440px', height='600px')).add(
bar_chart,
grid_opts=opts.GridOpts(pos_left="10",
pos_right="45%",
pos_top="50%",
pos_bottom="10")).add(
map_chart, grid_opts=opts.GridOpts()))
return g
if __name__ == '__main__':
data = read_file('test1.csv')
data = data_clear(data)
timeline = Timeline(init_opts=opts.InitOpts(
width="1440px", height="600px", theme=ThemeType.DARK))
for y in data.keys():
g = get_year_chart(y, data)
timeline.add(g, time_point=str(y))
timeline.add_schema(
orient="vertical",
is_auto_play=True,
is_inverse=True,
play_interval=500,
pos_left="null",
pos_right="5",
pos_top="20",
pos_bottom="20",
width="60",
label_opts=opts.LabelOpts(is_show=True, color="#fff"),
)
def draw(datelist, pricelist, title):
min_value = min(pricelist)
max_value = max(pricelist)
line = (
Line(init_opts=opts.InitOpts(
width='1800px',
height='800px',
js_host="./",
))
.set_global_opts(
title_opts=opts.TitleOpts(
title=title,
# subtitle='股票价格走势'
),
legend_opts=opts.LegendOpts(
is_show=True,
pos_top=10,
pos_left="center",
item_width=30,
item_height=15,
textstyle_opts=opts.TextStyleOpts(
font_family='Microsoft Yahei',
font_size=14,
font_style='oblique'
)
),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
xaxis_opts=opts.AxisOpts(
# type_="time",
name='日期',
split_number=10,
name_gap=35,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16,
font_family='Microsoft Yahei'
)
),
yaxis_opts=opts.AxisOpts(
type_="value",
# name='价格',
min_=min_value,
max_=max_value,
split_number=4,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16,
font_family='Microsoft Yahei'
),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1))
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
# xaxis_index=[0, 1],
range_start=30,
range_end=70,
),
opts.DataZoomOpts(
is_show=True,
# xaxis_index=[0, 1],
type_="slider",
pos_top="96%",
range_start=38,
range_end=70,
),
],
)
.add_xaxis(xaxis_data=datelist)
.add_yaxis(series_name="走势情况",
is_selected=True,
y_axis=pricelist,
label_opts=opts.LabelOpts(is_show=False)
)
.render(title + '.html')
)
def get(self):
uuid = self.get_argument('uuid', None)
deviceinfo = self.session.query(
ShorturlOverview.short_url_access_connectType.label('device'),
func.count(ShorturlOverview.short_url_id)).filter(
ShortUrlInfo.uuid == uuid).filter(
ShortUrlInfo.short_code ==
ShorturlOverview.short_url).group_by('device').all()
osinfo = self.session.query(
ShorturlOverview.short_url_access_osType.label('osinfo'),
func.count(ShorturlOverview.short_url_id)).filter(
ShortUrlInfo.uuid == uuid).filter(
ShortUrlInfo.short_code ==
ShorturlOverview.short_url).group_by('osinfo').all()
inner_data_pair = []
for device_data in deviceinfo:
dinfolist_item = []
dinfolist_item.append(device_data[0])
dinfolist_item.append(device_data[1])
inner_data_pair.append(dinfolist_item)
outer_data_pair = []
for os_data in osinfo:
os_data_item = []
os_data_item.append(os_data[0])
os_data_item.append(os_data[1])
outer_data_pair.append(os_data_item)
pie = Pie()
pie.add(
series_name="Device From",
data_pair=inner_data_pair,
radius=[0, "30%"],
label_opts=opts.LabelOpts(position="inner"),
)
pie.add(
series_name="OS Type From",
radius=["40%", "55%"],
data_pair=outer_data_pair,
label_opts=opts.LabelOpts(
position="outside",
formatter="{a|{a}}{abg|}\n{hr|}\n {b|{b}: }{c} {per|{d}%} ",
background_color="#eee",
border_color="#aaa",
border_width=1,
border_radius=4,
rich={
"a": {
"color": "#999",
"lineHeight": 22,
"align": "center"
},
"abg": {
"backgroundColor": "#e3e3e3",
"width": "100%",
"align": "right",
"height": 22,
"borderRadius": [4, 4, 0, 0],
},
"hr": {
"borderColor": "#aaa",
"width": "100%",
"borderWidth": 0.5,
"height": 0,
},
"b": {
"fontSize": 16,
"lineHeight": 33
},
"per": {
"color": "#eee",
"backgroundColor": "#334455",
"padding": [2, 4],
"borderRadius": 2,
},
},
),
)
pie.set_global_opts(
legend_opts=opts.LegendOpts(pos_left="left", orient="vertical"),
title_opts=opts.TitleOpts(title="Link Access Device Statistics",
pos_right="center"),
)
pie.set_series_opts(label_opts=opts.LabelOpts(is_show=False),
init_opts=opts.InitOpts(width="800px",
height="400px"),
tooltip_opts=opts.TooltipOpts(
trigger="item",
formatter="{a} <br/>{b}: {c} ({d}%)"))
pie.render(path=settings['template_path'] + '/pie.html',
encoding='utf-8')
self.render('pie.html')
"itemStyle": {
"color": "#e75b68"
}
},
{
"name": "Sweet Aromatics",
"value": 1,
"itemStyle": {
"color": "#d0545f"
}
},
],
},
]
c = (Sunburst(init_opts=opts.InitOpts(width="1000px", height="600px")).add(
"",
data_pair=data,
highlight_policy="ancestor",
radius=[0, "95%"],
sort_="null",
levels=[
{},
{
"r0": "15%",
"r": "35%",
"itemStyle": {
"borderWidth": 2
},
"label": {
"rotate": "tangential"
3D 地球
"""
import pyecharts.options as opts
from pyecharts.faker import POPULATION
from pyecharts.charts import MapGlobe, Grid
from pyecharts.globals import ThemeType
high = max([x for _, x in POPULATION[1:]])
low = min([x for _, x in POPULATION[1:]])
m = (
MapGlobe(init_opts=opts.InitOpts(
width="1920px",
height="900px",
page_title="全球互联网网络地图",
theme=ThemeType.WALDEN)).add_schema().add(
maptype="world",
series_name="World Population",
data_pair=POPULATION[1:],
is_map_symbol_show=False,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
# title_opts=opts.TitleOpts(title="全球互联网网络AS号分布图"),
visualmap_opts=opts.VisualMapOpts(
min_=low,
max_=high,
range_text=["max", "min"],
is_calculable=True,
# is_piecewise=True,
def trans_data_to_use(dataset: list):
size = len(dataset)
# 元组中可能数据大小不一致，因此先进行转化
dataset = np.array(dataset)
k = 1 / 1024
# 得到测试的时刻
x_data = dataset[:, 10]
y_data_1 = np.array(dataset[:, 1], float) * k
y_data_1 = np.round(y_data_1, decimals=1)
y_data_2 = np.array(dataset[:, 2], float) * k
y_data_2 = np.round(y_data_2, decimals=1)
y_data_3 = np.array(dataset[:, 3], float) * k
y_data_3 = np.round(y_data_3, decimals=1)
y_data_4 = np.array(dataset[:, 4], float) * k
y_data_4 = np.round(y_data_4, decimals=1)
y_data_5 = np.array(dataset[:, 5], float) * k
y_data_5 = np.round(y_data_5, decimals=1)
y_data_6 = np.array(dataset[:, 6], float) * k
y_data_6 = np.round(y_data_6, decimals=1)
y_data_7 = np.array(dataset[:, 7], float) * k
y_data_7 = np.round(y_data_7, decimals=1)
y_data_8 = np.array(dataset[:, 8], float) * k
y_data_8 = np.round(y_data_8, decimals=1)
(Line(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="java_heap",
y_axis=y_data_1,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="native_heap",
y_axis=y_data_2,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="code",
y_axis=y_data_3,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="stack",
y_axis=y_data_4,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="graphics",
y_axis=y_data_5,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="private_other",
y_axis=y_data_6,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="system",
y_axis=y_data_7,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).add_yaxis(
series_name="total",
y_axis=y_data_8,
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]),
).set_global_opts(
title_opts=opts.TitleOpts(title="memory information",
pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="item",
formatter="{a} <br/>{b} : {c}"),
legend_opts=opts.LegendOpts(pos_left="left"),
xaxis_opts=opts.AxisOpts(type_="category", name="x"),
yaxis_opts=opts.AxisOpts(
type_="log",
name="y",
splitline_opts=opts.SplitLineOpts(is_show=True),
is_scale=True,
),
).render("memory2.html"))
"Latin": 9,
"Religious": 10,
"New Age": 11,
"International": 12,
"Classical": 13,
"Avant-Garde": 14,
}
for key in cc:
cat.append({"name": key, "symbol": "circle"})
# %%
c = (
Graph(
init_opts=opts.InitOpts(width="100%", height="700px",
renderer="canvas" # 渲染模式 svg 或 canvas，即 RenderType.CANVAS 或 RenderType.SVG
),
).add(
"",
nodes_data,
links_data,
categories=cat,
repulsion=50,
linestyle_opts=opts.LineStyleOpts(curve=0.2),
label_opts=opts.LabelOpts(is_show=False),
edge_symbol=['none', 'arrow'],
is_draggable=True
).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
# title_opts=opts.TitleOpts(title="Graph-GraphNode-GraphLink-WithEdgeLabel"),
